The Barents Oscillation and its impact on the Arctic climate

Transcription

1 Master Degree Project in Meteorology, 30 ECTS credits The Barents Oscillation and its impact on the Arctic climate Hans Chen Supervisors: Heiner Körnich Qiong Zhang Department of Meteorology Stockholm University July 3, 2012

2 Abstract The second mode of the wintertime sea level pressure anomaly field in mid- and high latitudes of the Northern Hemisphere, the Barents Oscillation, is coupled to the meridional flow over the Nordic Seas and may have important implications for the Arctic climate variability. It has also been suggested that the Barents Oscillation can be attributed to an eastward shift of the leading pattern, the Arctic Oscillation/North Atlantic Oscillation. This study investigates the impact of the Barents Oscillation on the Arctic climate and the relation between the Barents Oscillation and the North Atlantic Oscillation using reanalysis data. A robust atmospheric variability pattern resembling the Barents Oscillation is found from the empirical orthogonal analysis, and its positive phase is associated with southerly wind anomalies over the Nordic Seas, westerly wind anomalies in the Barents Sea, reduced sea ice in the Barents Sea and Denmark Strait, and a warming over Greenland, the Nordic Seas, and the Barents Sea. Analyzing the North Atlantic Oscillation before and after the shift of the action centers revealed that the eastward shift in the leading mode has a similar impact on the climate as the Barents Oscillation. The conclusion is that the Barents Oscillation and North Atlantic Oscillation are most likely related. The non-stationarity of the North Atlantic Oscillation in terms of the action center positions has a large impact on the climate variability at northern high latitudes, and the Barents Oscillation may be useful for expressing this non-stationarity.

3 1 Introduction Dramatic changes in the Arctic climate have been observed in the recent decades, including rising surface air temperature (SAT) with a trend of up to 2 C per decade in spring (Rigor et al., 2000; Chapman and Walsh, 1993), and substantial loss of sea ice (e.g. Cavalieri, 2003). These trends are often seen as an amplified response to global warming due to positive feedbacks and other processes in the Arctic, a phenomenon commonly referred to as polar amplification. The Arctic climate is thus an important indicator of global climate change. However, changes in Arctic sea ice extent also affect the atmospheric and ocean circulation Bengtsson et al. (e.g. 2004); Aagaard and Carmack (e.g. 1989). and there is evidence that sea ice variations can influence the global climate (Walsh, 1983). A large part of the recent trends in the Arctic can be explained by human activity, i.e., increasing the greenhouse gas concentration in the atmosphere (Vinnikov, 1999; Johannessen et al., 2004). In addition to this, the Arctic climate system has its own internal variability with marked interannual to multidecadal variability (e.g. Johannessen et al., 2004). Bengtsson et al. (2004), for example, suggested that the large warming in the early twentieth century was caused by natural fluctuations in the climate system. The warming had its largest amplitude in the Arctic (Johannessen et al., 2004) with a maximum area averaged temperature increase of about 1.7 C compared to Using an atmospheric model, Bengtsson et al. (2004) found that a large part of the warming was likely due to a retreat of sea ice in the Barents Sea. Other studies (e.g. Goosse and Holland, 2005) have found that anomalous heat transport in the Barents Sea plays a dominant role in driving changes in the Arctic SAT. The atmospheric and oceanic transport of heat are both related to the atmospheric circulation, as the inflow of warm water into the Barents Sea is largely driven by surface winds. In the context of the early twentieth century warming Bengtsson et al. (2004) found that the temperature increase was associated with enhanced westerly winds between Spitsbergen and Norway, which lead to increased oceanic heat transport into the Barents Sea and a subsequent retreat of sea ice in this region. The reduced sea ice cover further amplified the warming due to increased turbulent heat release from the ocean to the atmosphere during the cold season. This heat source generated a vorticity source in the lower troposphere which sustained the westerlies into the Barents Sea and advected warm air to other parts of the 2

4 Arctic. Many studies on the variability of the Arctic climate have focused on the North Atlantic Oscillation (NAO) and the closely related Arctic Oscillation (AO (Holland, 2003; Jung and Hilmer, 2001, among others)), which are the leading modes of atmospheric variability in the Northern Hemisphere (Thompson and Wallace, 1998). They are characterized by a seesaw in surface pressure between mid- and high latitudes. A positive NAO/AO index is associated with negative sea level pressure (SLP) anomalies in high latitudes, stronger westerly winds in mid- and high latitudes, temperature increase over a large part of the Eurasia continent, among other things (Thompson and Wallace, 2000). Studies have also found a dipole pattern in sea ice cover due to the NAO, with a cooling and increased ice cover in the Labrador Sea during the positive phase while the Barents and Greenland Sea experience a warming and reduced sea ice cover (e.g. Strong and Magnusdottir, 2009). The large Arctic warming that started in the 1920s could not be explained by the NAO, which had a negative index and should have supported a cooling at the time when the temperatures started to rise (Bengtsson et al., 2004). Instead, it was noted that the regression pattern of SLP anomalies related to the oceanic volume inflow to the Barents Sea resembled the Barents Oscillation (BO), an atmospheric variability pattern identified by Skeie (2000). The BO is found as the second leading Empirical Orthogonal Function (EOF) of monthly winter SLP anomalies polewards of 30 N and is related to the variability of the meridional flow as well as the sensible heat in the Nordic Seas. The circulation pattern could therefore be of high relevance for the variability in the Barents Sea. However, there has been no thorough study of how the BO influences the Arctic climate. It has been pointed out that the BO cannot be found during certain time periods (Skeie, 2000; Tremblay, 2001) which indicates that it is not a stable mode of atmospheric variability. Furthermore, Tremblay (2001) used a toy model to show that a BO-like pattern can arise from a regime shift in the leading mode of variability, the AO, and suggested that the BO and AO cannot be considered physically independent variability modes. The objective of this study was to perform a deeper investigation of the BO in connection with Arctic climate variability. The data and methods used in this study are described in Section 2. In Section 3.1, the Barents Oscillation is identified and the robustness of the pattern is tested using different datasets and different time periods. The impact of the BO on the 3

5 climate, particularly in the Barents Sea and nearby regions, is then studied using reanalysis data in Section 3.3, and the relationship between the NAO and BO is evaluated in Section 3.4 by analyzing the NAO in different periods of the dataset. Finally, some possible mechanisms for the BO and shift in NAO are given in Section Data and methods 2.1 Reanalysis data This study mainly used monthly mean sea level pressure, 10-m wind, and 2-m SAT from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) Reanalysis 1 dataset (Kalnay et al., 1996) for the period Observational data from the Arctic are relatively sparse and SLP measurements from drifting stations were not routinely incorporated until the 1950s (Hastings, 1971) which is why earlier data were not used in our analyses. The horizontal resolution is for SLP while wind and SAT data had a resolution of The sea ice measurements were obtained from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset (Rayner, 2003) as monthly sea ice concentration on an grid. Finally, two datasets were used to examine if the results from NCEP/NCAR Reanalysis 1 were reproducible, the ERA-Interim reanalysis (Dee et al., 2011) from European Centre for Medium-Range Weather Forecasts (ECMWF) with a horizontal resolution for , and National Oceanic and Atmospheric Administration (NOAA) Twentieth Century Reanalysis (Compo et al., 2011) with resolution spanning All data were detrended prior to analysis. This did not affect the results significantly, changes were mostly noted in the amplitudes. 2.2 Empricial orthogonal function analysis Emprical orthogonal function (EOF) analysis was used to decompose SLP anomalies into a spatial pattern, the EOF, and a time-dependent principal 4

6 component (PC) as follows: Z(x, y, t) = N P C(t) EOF (x, y). k=1 EOFs are computed from the eigenvalues of the covariance matrix so that the first EOF, which is simply the first eigenvector, explains the largest possible amount of the variance, the second EOF explains the largest amount of the remaining variance, and so on. The eigenvectors are by construction orthogonal, i.e., their scalar product is 0. Before the EOF analysis the variance is usually weighted with the grid area to ensure that the same emphasis is put on low and high latitudes. The amount of variance explained by the nth EOF is given by its corresponding eigenvalue λ n : 100λ n p k=1 λ k %, i.e., the eigenvalue divided by the sum of all eigenvalues. The PCs are obtained by projecting the anomaly field onto a particular EOF. By normalizing the PCs to one standard deviation, the EOFs show the anomalies associated with one positive standard deviation of their PC. We constructed annual SLP anomalies for the months December through March (DJFM) to focus on the interannual variability in winter. With monthly values, the intraseasonal variability is also included in the EOF analysis. It was however necessary to use monthly values when comparing with patterns that only have monthly indices, since there is no good way to convert a monthly index to annual values.. EOF analyses were performed on the SLP anomalies in the region polewards of 30 N between 90 E and 90 W, as our primary interest lies in the interaction between the North Atlantic Ocean and Barents Sea. There is another benefit to restricting the region like this which will be discussed in Section Linear regression analysis To investigate the impact of the EOF patterns on the climate, we linearly regressed wind, SAT, and sea ice concentration anomalies on the normalized 5

7 principal components. The resulting regression maps then show the anomalies corresponding to one positive standard deviation of a particular PC. In order to determine if the results were significant we used the F-test. 3 Results and discussion 3.1 Atmospheric circulation pattern The first two EOFs of annual wintertime SLP anomalies from the NCEP/NCAR Reanalysis 1 are shown in Figure 1. EOF 1 is the well-known NAO with the Azores high and Icelandic low. It accounts for 49.6% of the total variance for the chosen domain and is well separated from the other modes according to North s rule of thumb North et al. (1982). Figure 1b shows the second EOF which we will call the Barents Oscillation (BO).. It has a quadrupole structure with its main center of action over the Scandinavia Peninsula, a center of opposite sign over Greenland, and two additional weak centers over the North Atlantic Ocean. Regression of geopotential height on PC 2 at different pressure levels (not shown) reveals that the BO pattern extends above the 300 hpa level and that it displays a quasi-barotropic structure, just like the NAO. The second EOF explains 13.2% of the variability. According to North s rule of thumb it is separated from the third and fourth EOF (9.9% and 9.3%, respectively) since the spacing between the second and third eigenvalue is larger than the estimated sampling error, but it is not as well separated as the first mode. EOFs whose eigenvalues are closely spaced are likely to be mixed due to sampling errors and one should be careful when trying to interpret them physically. PC 2 in Figure 1d shows a large variability on interannual and decadal time scales. There are no significant trends, however, one may notice that the amplitude is smaller for the most recent years. Detrending the data had no large influence on the results. Patterns similar to EOF 2 (Figure 1b) could be found in the ERA-Interim and Twentieth Century Reanalysis datasets which are not shown here. To test the stability of the BO found in this study, the SLP anomalies from 6

8 (a) (b) (c) (d) Figure 1: First (left panels) and second (right panels) EOF of annual wintertime (DJFM) SLP anomalies and their corresponding PCs. The patterns in (a) and (b) are associated with the NAO and BO, respectively, and show the SLP variations associated with one positive standard deviation of their respective PC (hpa). Panel (c) and (d) show the normalized PCs as dashed lines, with thick lines for the five-year running mean. Twentieth Century Reanalysis were divided into five contiguous 28-year time periods, and an EOF analysis was performed on each period. The outcome is shown in Figure 2. Even though the results for the earlier periods are less reliable due to fewer observations, the obtained EOFs are supported by the physics and dynamics in the NCEP Climate Forecast System model (Saha et al., 2010) used in the construction of the reanalysis dataset. The second EOF shows a similar tripole pattern in all periods (Figure 2a d) except in the most recent one. This is likely related to the change in the leading mode, with a noticeable eastward shift of the NAO centers in recent decades (illustrated in Figure 7 for two slightly different periods). We also notice that the BO in Figure 1b is approximately the mean of the two patterns in Figure 2d and 2e. It is possible that the small PC 2 amplitude in recent 7

9 (a) (b) (c) (d) (e) Figure 2: EOF 2 of annual winter (DJFM) SLP anomalies (hpa) for the period (a) , (b) , (c) , (d) , and (d)

10 years is due to changes in the atmospheric circulation pattern. The meridional structure over the Nordic Seas seems to be persistent in all periods, while the pattern over the Barents Region changes with time. Out of all patterns, the second EOF in the last period (Figure 2e) appears to be most related to westerlies into the Barents Sea due to the large meridional SLP gradient in this region, whereas the other circulation patterns have a more zonal gradient which favors a meridional geostrophic flow. Figure 2 demonstrates that our EOF 2 can be reproduced during different time periods, or in other words, it is a stable mode. It also reveals that there is a large shift in the pattern in recent decades. We will return to the shift in the leading mode, the NAO, later in Section Comparison with the Barents Oscillation and other similar circulation patterns The major differences in our EOF analysis compared to Skeie (2000) is that we used annual winter SLP anomalies instead of monthly data to remove the intraseasonal variability, and the region in this study was further restricted to be between 90 W and 90 E. With monthly data the second EOF looks similar to Figure 1b, but with the centers slightly shifted. However, when using the temporal and spatial data selection as the original study by Skeie (2000), the obtained EOF 2 (Figure 3a) is remarkably different from the original BO, their Figure 1b. Repeating the calculations but without weighting the variance with the grid area when performing the EOF analysis, the resulting second leading EOF looks much more similar to the BO pattern found by Skeie (2000) (compare Figure 3b with their Figure 1b). It is therefore likely that the BO is the result of an unweighted EOF analysis. This is a problem with gridded data in high latitudes, since the grid points get closer polewards and thus more emphasis is placed on the variance closer to the poles. Despite this flaw, the the pattern found by Skeie (2000) defined the BO. It may explain why the BO is difficult to reproduce during some time periods, and thus we prefer the method above, with area-weighted EOF over a limited area over the North Atlantic Ocean. Skeie (2000) mentioned that the BO is most similar to a previously recognized 9

11 (a) (b) Figure 3: Reproduced Barents Oscillation (a) with area weighting and (b) without area weighting (hpa). teleconnection pattern called the Scandinavia pattern (SCAND), referred to as the Eurasia-1 pattern by Barnston and Livezey (1987). A correlation coefficient of r = 0.65 was found between their second PC and the SCAND index. To compare PC 2 from this study with the monthly SCAND index, we had to repeat the EOF analysis using monthly SLP anomalies for the winter months (DJFM). The SCAND index was obtained from NOAA, and a strong correlation was found between the index and our PC 2 (r = 0.72 at the 99% confidence level) for the period This means that about 50% of the variance associated with the two patterns are related. Thus, the BO and SCAND are related but not identical. Another circulation pattern that could be related to our EOF 2 is the dipole pattern found by Wu et al. (2006). It is the second EOF of monthly SLP anomalies polewards of 70 N for the months October to March, and is related to the sea ice transport in the Arctic. When extending our EOF analysis to include October and November and again using monthly values, we found a 0.67 correlation at the 99% confidence level between PC 2 and the dipole pattern for the period used by Wu et al. (2006), So, again the patterns are related but still with some differences. As we are interested in the variability in the Barents Sea region, we continue with our definition of the BO (Figure 1b). 10

12 3.3 Impact on the Arctic climate To investigate the influence of the BO found in this study on the Arctic climate, the surface wind, sea ice concentration, and SAT were regressed on PC 2. The regression maps in Figure 4 show how the anomalies vary linearly with PC 2 (Figure 1d), and since the principal component has been normalized to one standard deviation, the maps show the anomalies corresponding to one positive standard deviation of PC 2. The positive phase of the BO is associated with southerly wind anomalies over the Nordic Seas and westerly anomalies in the Barents Sea (Figure 4a). There is also anomalous wind from the north over large parts of Europe. The anomalous winds may play an important role for the wind-driven ocean circulation, temperature advection, and sea ice transport, as we will see later. The largest anomalous winds are found in the Nordic Seas with wind speeds reaching 1.6 m/s for one standard deviation of PC 2. Figure 4b shows a similar dipole in sea ice as found by Strong and Magnusdottir (2009). In their study they concluded that the dipole is driven by the NAO. Here, the reduced sea ice in the Denmark Strait can be explained by the advection of warm air due to the anomalous southerly winds over the Nordic Seas. In the Labrador Sea there are also southerly wind anomalies at the surface, but a decrease in sea ice. To explain this the typical temperature advection was calculated at 850 hpa using the advection equation (the scalar product between the wind vector field and the horizontal temperature gradient with switched sign). The climatological mean temperature in winter (DJFM) at 850 hpa and the wind anomalies associated with PC 2 on the same pressure level were used in the calculation, and the result can be seen in Figure 5. Figure 5b reveals that the temperature advection is negative over the Labrador Sea, supporting a growth of sea ice during the positive BO phase in this region. The strongest advection is found in the Denmark Strait where the the wind anomalies and temperature gradients are large. From the temperature advection map it is not clear why the largest sea ice decrease is found in the Barents Sea, a significant decrease of about 8 percent points. The advection is small here due to mostly zonal wind anomalies and weak temperature gradients. The melting of sea ice may therefore be the result of wind-driven inflow of Atlantic water into the Barents Sea, as described by e.g. Bengtsson et al. (2004). Unfortunately, to the author s knowledge, there are no long-term measurements of the oceanic inflow into the Barents Sea. Results from models could instead be used to examine the 11

13 (a) (b) (c) Figure 4: Linear regression of DJFM (a) surface wind (shading shows the absolute wind speed in m/s), (b) sea ice concentration (fraction), and (c) surface air temperature ( C) on PC 2. Regions within contours are statistically significant at the 95 % confidence level. 12

14 relation between PC 2 and the oceanic volume transport through the Barents Sea Opening. (a) (b) Figure 5: (a) Climatological mean DJFM temperature (shading, C) and wind anomalies associated with PC 2 (vectors) at 850 hpa. (b) Temperature advection ( C/s) calculated from (a). Positive values correspond to a heating due to the temperature advection. The SAT shows a similar response to the BO as the sea ice changes (Figure 4c), with a warming over the Nordic Seas, Barents Sea, and a large part of Greenland, as well as a temperature decrease over the Labrador Sea. Additionally, during years with positive PC 2 there is a cooling over a large part of Europe due to the northerly wind anomalies, which can also been seen in the temperature advection plot (Figure 5b). 3.4 Relation to the North Atlantic Oscillation The robustness of the BO has been demonstrated in Section 3.1, and the impact of the circulation pattern was studied in Section 3.3. However, while the EOF method is mathematically correct, it does not contain any physics. It is therefore important to find a physical interpretation for circulation patterns found using this method. Tremblay (2001) suggested that the BO appears due to the non-stationarity of the AO. Although EOFs are constructed to be orthogonal and should thus be unrelated, they showed that it is possible to construct an idealized case where the first and second PC are perfectly correlated during one period, and perfectly anti-correlated the rest of the time. The correlation coefficient for 13

15 the whole period is then 0. This was connected to the BO/AO relationship, and it was noted that the correlation between the PCs corresponding to the AO and BO patterns was negative during , but positive for the period. To examine the relationship between our PC 1 and PC 2 we calculated a moving correlation between the two principal components with a 15-year time window, as shown in Figure 6. There is a clear trend, with the moving correlation being positive until around 1975 and then consistently negative for the second period. Whether the correlation is positive or negative only depends on sign convention when defining the EOFs. If the BO and AO/NAO truly are unrelated, it seems unlikely that the moving correlation would show such a trend. Rather we would expect it to be around 0, sometimes positive, and sometimes negative. Figure 6: PC 1, PC 2, and the moving correlation between the two with a time window of 15 years. The correlation is slightly positive (around 0.2) until 1975 after which it switches sign and remains negative (around 0.4) for the rest of the period. Clearly something happened around If two EOF analyses are carried out, one for and the second for , one notices a clear regime shift in the leading EOF of wintertime (DJFM) SLP anomalies, see Figure 7a and 7b. The shift is more apparent in Figure 7c which shows the difference between the two periods (again, the sign is just a matter of how the EOFs are defined and is not important). This pattern is strikingly similar to 14

16 the BO pattern (Figure 1b), which lends credence to the claim by Tremblay (2001) that the BO appears due to a shift in the AO/NAO. (a) (b) (c) Figure 7: EOF 1 of annual winter SLP anomalies (hpa) for the time period (a) , (b) , and (c) the difference between (a) and (b). The next question is then, does the shift in the AO/NAO have a similar impact on climate as the BO? Figure 8 shows the regression of sea ice concentration on PC 1 corresponding to the NAO pattern for and In the first period, (Figure 8a), a similar dipole in sea ice as reported by Strong and Magnusdottir (2009). The positive phase of the NAO gives rise to an increase of sea ice in the Labrador Sea, and a decrease in the Greenland Sea and Barents Sea. After the shift, however, the sea ice cover in the Greenland Sea and Barents Sea is no longer driven by the NAO to such a large extent. Instead, there is a better relation between the sea ice concentration in the Baltic Sea and PC 1. The region of sea ice growth during the positive phase has also extended northward to the west coast of Greenland. The difference in Figure 8c resembles the impact of the BO on 15

17 sea ice (Figure 4b), but with larger sea ice loss particularly in the Greenland Sea, and a smaller region of sea ice increase in the Labrador Sea. (a) (b) (c) Figure 8: Linear regression of sea ice concentration (fraction) on PC 1 (DJFM). (a) , (b) , and (c) the difference between (a) and (b). Regions within contours are statistically significant at the 95 % confidence level. The regression of SAT on PC 1 for the two periods shows a east-west tripole of SAT anomalies (Figure 9). Years with positive NAO index are associated with anomalous westerly winds over the North Atlantic Ocean, which advect warm air from the ocean over the relatively cold European continent and cause a warming over a large part of Europe. The strong cooling in the region including Greenland, part of Canada, and the area in between can be explained by the meridional structure of the NAO pattern over this part, bringing cold air from the north when the NAO is in its positive phase. During the first time period ( ) the NAO is associated with a significant warming in the Greenland Sea and Barents Sea, see Figure 9a. 16

18 (a) (b) (c) Figure 9: Linear regression of surface air temperature ( C) on PC 1 (DJFM). (a) (b) (c) the difference between (a) and (b). Regions within contours are statistically significant at the 95 % confidence level. This is not seen for the period in Figure 9b. The increase in SAT may have been amplified due to the sea ice decrease corresponding to the positive phase of the NAO in the first period (Figure 8a), and the associated turbulent heat release from the ocean to the atmosphere. The warming in high latitudes stands out when taking the difference between the SAT regression maps between the two time periods, as shown in Figure 9c. Over most of the other regions the SAT difference is negative. The pattern is, again, similar to the regression on PC 2 (Figure 4c). In other words, the shift in the NAO has a similar impact on SAT as the BO. However, there are also a few dissimilarities. First, the SAT in a larger part of the Nordic Seas is associated with the BO than due to the shift in the NAO. Second, the BO gives rise to a larger relative cooling over Europe. These differences 17

19 may result from the second EOF picking up variance not associated with the NAO shift. From this we learn that the shift in the AO/NAO has important implications for the climate, especially in high latitudes. From 1976 onwards the sea ice changes in the Denmark Strait and Barents Sea are no longer driven by the leading EOF, and this change is also seen in the SAT response. The NAO index is usually calculated as the pressure difference between two stations, or by projecting e.g. 500 hpa high anomalies onto a (rotated) EOF for a pre-defined time period (which yields the principal component of the EOF). Thus the non-stationarity of the NAO is not taken into account. The BO may therefore be important for expressing the shift in the NAO. One should also be careful when using the NAO for a certain period and extrapolating it into the future, e.g. by interpreting the corresponding PC. 3.5 Mechanism In Section 3.4 it was shown that the BO most likely is related to a regime shift in the leading EOF, the NAO. However, the question still remains: What is the mechanism behind the BO and the eastward shift of the NAO? Here we can only offer qualitative speculations. Earlier it was shown that there is a strong relationship between the BO and the SCAND (see Section 3.2). Bueh and Nakamura (2007) studied the mechanism behind the SCAND and found that it is associated with Rossby wave propagation through the Scandinavia Peninsula and feedback forcing from high-frequency transient eddies along the Atlantic storm track. The BO may be generated by a similar mechanism. However, it does not explain the large change in the BO and NAO pattern in recent decades. Another explanation could be due to anomalous heat fluxes in high latitudes. Bengtsson et al. (2004) used model results to show how increased turbulent heat fluxes due to reduced sea ice generated a cyclonic vortex over the Barents Sea. The shift in the NAO occurred in recent decades when the average sea ice extent in the Arctic was well below average, most likely due to global warming (e.g. Vinnikov, 1999). In winter the anomalous heating from the ocean may therefore have changed the atmospheric circulation pattern, which could manifest itself as a shift in the NAO. It would be interesting to see if general circulation models can reproduce the 18

20 BO and the shift in the NAO. One benefit of models is that the anthropogenic forcing can be removed, which makes it possible to investigate if the recent shift in the leading mode is due to natural variability or related to human activity. 4 Summary and conclusions The Barents Oscillation is an atmospheric circulation pattern that is linked to the meridional flow over the Nordic Seas (Skeie, 2000), and studies have noted that it shares similarities with the heat transport variability in the Barents Sea (Goosse and Holland, 2005; Bengtsson et al., 2004). It could therefore have important consequences for the variability in the Arctic climate. However, it has also been suggested that the BO appears due to the non-stationarity of the leading AO/NAO pattern and that the two modes of atmospheric variability cannot be considered independent (Tremblay, 2001). The aim of this study was to examine the impact of the BO on the Arctic climate on interannual to decadal time scales, and investigate the relationship between the BO and the NAO. We identified the BO as the second EOF of annual SLP anomalies for the winter months December through March in the region 30 N 90 N and 90 E 90 W. The atmospheric variability pattern was found to be robust in the NCEP/NCAR Reanalysis 1, Twentieth Century Reanalysis, and ERA-Interim datasets, and for different time periods from 1871 to The positive phase of the BO is associated with westerly wind anomalies in the Barents Sea and southerly over the Nordic Seas. The winds were found to be important for the meridional heat transport and likely also for the inflow of Atlantic water into the Barents Sea. This was seen in the SAT response which showed a warming in high latitudes, particularly in the Barents Sea, Nordic Seas and over Greenland. The warming in the Barents Sea and Denmark Strait may have been amplified by the associated sea ice decrease in these regions. Finally, it was shown that the BO is most likely related to the non-stationarity of the NAO pattern. During the BO had a similar impact on SLP, sea ice, and SAT as the eastward shift in the NAO that occurred between the time periods and This has important implications for the Arctic climate, as the relationship between the NAO and the sea ice changes in the Barents and Greenland Sea appears to be weaker in the last sub-period compared to the first period. Thus, the BO may be important for 19

21 describing the non-stationarity of the NAO. Acknowledgments First and foremost, I am deeply grateful to my supervisors Heiner Körnich and Qiong Zhang for their valuable guidance, encouragement, and support during the whole project. Furthermore I would like to thank Tinghai Ou for interesting discussions about the dynamics in the Arctic, and for reproducing the EOF results independently. Thanks also to Léon Chafik for his help with the ocean part and Paul Skeie for answering questions about his paper. NCEP/NCAR Reanalysis 1 and Twentieth Century Reanalysis data were provided by the NOAA/OAR/ESRL PSD, ERA-Interim by ECMWF, and HadISST by UK Met Office. References Aagaard, K. and Carmack, E. C. (1989). The role of sea ice and other fresh water in the Arctic circulation. Journal of Geophysical Research, 94(C10): Barnston, A. G. and Livezey, R. E. (1987). Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Monthly Weather Review, 115(6): Bengtsson, L., Semenov, V. A., and Johannessen, O. M. (2004). The early twentieth-century warming in the Arctic A possible mechanism. Journal of Climate, 17: Bueh, C. and Nakamura, H. (2007). Scandinavian pattern and its climatic impact. Quarterly Journal of the Royal Meteorological Society, 133(629): Cavalieri, D. J. (2003). 30-year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability. Geophysical Research Letters, 30(18). Chapman, W. L. and Walsh, J. E. (1993). Recent variations of sea ice and air temperature in high latitudes. Bulletin of the American Meteorological Society, 74(1):

22 Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J. (2011). The Twentieth Century Reanalysis Project. Quarterly Journal of the Royal Meteorological Society, 137(654):1 28. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J., Park, B., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J., and Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656): Goosse, H. and Holland, M. M. (2005). Mechanisms of decadal Arctic climate variability in the Community Climate System Model, version 2 (CCSM2). Journal of Climate, 18(17): Hastings, A. D. (1971). Surface Climate of the Arctic Basin: Selected Climatic Elements Related to the Performance of Surface-effect Vehicles. U.S. Army Engineer Topographic Laboratories. Holland, M. M. (2003). The North Atlantic Oscillation/Arctic Oscillation in the CCSM2 and its influence on Arctic climate variability. Journal of Climate, 16(16): Johannessen, O. M., Bengtsson, L., Miles, M. W., Kuzmina, S. I., Semenov, V. A., Alekseev, G. V., Nagurnyi, A. P., Zakharov, V. F., Bobylev, L. P., Pettersson, L. H., Hasselmann, K., and Cattle, H. P. (2004). Arctic climate change: observed and modelled temperature and sea-ice variability. Tellus A, 56(4): Jung, T. and Hilmer, M. (2001). The link between the North Atlantic Oscillation and Arctic sea ice export through Fram Strait. Journal of Climate, 14(19): Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., 21

23 Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D. (1996). The NCEP/NCAR 40-Year reanalysis project. Bulletin of the American Meteorological Society, 77(3): North, G. R., Bell, T. L., Cahalan, R. F., and Moeng, F. J. (1982). Sampling errors in the estimation of empirical orthogonal functions. Monthly Weather Review, 110(7): Rayner, N. A. (2003). Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research, 108(D14). Rigor, I. G., Colony, R. L., and Martin, S. (2000). Variations in surface air temperature observations in the Arctic, Journal of Climate, 13(5): Saha, S., Moorthi, S., Pan, H., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y., Chuang, H., Juang, H. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M. (2010). The NCEP Climate Forecast System Reanalysis. Bulletin of the American Meteorological Society, 91(8): Skeie, P. (2000). Meridional flow variability over the Nordic Seas in the Arctic Oscillation framework. Geophysical Research Letters, 27(16):2569. Strong, C. and Magnusdottir, G. (2009). Modeled winter sea ice variability and the North Atlantic Oscillation: a multi-century perspective. Climate Dynamics, 34(4): Thompson, D. and Wallace, J. (1998). The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophysical Research Letters, 25(9): Thompson, D. W. J. and Wallace, J. M. (2000). Annular modes in the extratropical circulation. Part I: Month-to-Month variability. Journal of Climate, 13(5):

24 Tremblay, L. (2001). Can we consider the Arctic Oscillation independently from the Barents Oscillation? Geophysical Research Letters, 28(22): Vinnikov, K. Y. (1999). Global warming and Northern Hemisphere sea ice extent. Science, 286(5446): Walsh, J. E. (1983). The role of sea ice in climatic variability: Theories and evidence 1. Atmosphere-Ocean, 21(3): Wu, B., Wang, J., and Walsh, J. E. (2006). Dipole anomaly in the winter Arctic atmosphere and its association with sea ice motion. Journal of Climate, 19(2):